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Image Texture Defect Detection Method Using Fuzzy C-Means Clustering for Visual Inspection Systems

  • Research Article - Electrical Engineering
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Abstract

A new texture defect detection method for automatic visual inspection systems is presented in this paper. It divides an analysed texture image into non-overlapping samples, and then calculates features of each sample using the Principle Component Analysis technique. Finally, the fuzzy c-means clustering of these features is applied to classify the sample as defective or non-defective. Unlike many existing methods, the proposed scheme does not require a training step to collect defective and non-defective texture samples. The experimental results show that the method is at least as effective and accurate as many existing methods for image texture defect detection.

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Authors and Affiliations

  1. Institute of Applied Computer Science, Lodz University of Technology, Stefanowskiego 18/22, 90-924, Lodz, Poland

    Volodymyr Mosorov & Lukasz Tomczak

Authors
  1. Volodymyr Mosorov
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  2. Lukasz Tomczak
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Correspondence to Volodymyr Mosorov.

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Mosorov, V., Tomczak, L. Image Texture Defect Detection Method Using Fuzzy C-Means Clustering for Visual Inspection Systems. Arab J Sci Eng 39, 3013–3022 (2014). https://doi.org/10.1007/s13369-013-0920-7

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  • DOI: https://doi.org/10.1007/s13369-013-0920-7

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